Neural Network Processor Conditional Layers Early Termination
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Solution Overview
Problem
Neural networks face inefficiencies due to the need to execute all layers for every input, leading to high computational requirements and slow response times, especially when intermediate results can suffice for task completion.
Innovation Solution
Incorporating conditional layers in neural network processors that allow for early termination of computations based on threshold conditions, using specialized integrated circuits with arrays of processing engines and memory banks for fast storage and execution of weight values and instructions, enabling parallel computations and reducing unnecessary processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all layers of the neural network are executed for every input, then the computation is thorough and accurate, but the computational requirements increase and response time slows
Solution Approach 1:
The patent applies partial action by executing only a subset of neural network layers based on intermediate results. When an intermediate result meets a predetermined condition (e.g., confidence threshold), the system terminates computation early and outputs the result, avoiding unnecessary execution of remaining layers. This resolves the contradiction by performing sufficient computation to ensure accuracy when needed, while reducing computation to improve response time when intermediate results are already adequate.
2Reliability
If all layers of the neural network are executed for every input, then the computation is complete, but the computational requirements and processing time increase
Solution Approach 1:
The system implements feedback by continuously monitoring intermediate results during layer execution and comparing them against predetermined conditions. When the feedback indicates that the intermediate result satisfies the condition (e.g., achieves sufficient accuracy or confidence level), the system terminates computation. This feedback mechanism ensures computation completeness when necessary while minimizing processing time when intermediate results are adequate.
3Productivity
If conditional layers are added to allow early termination, then processing efficiency improves, but the device complexity increases
Solution Approach 1:
The patent segments the neural network processor into distinct functional units: standard processing layers and conditional layers. Each conditional layer is designed as a modular component that receives intermediate results, evaluates them against predetermined conditions, and controls the flow of computation. This segmentation allows the system to achieve high processing efficiency through early termination while managing device complexity through modular, standardized conditional layer designs that can be integrated into existing neural network architectures.
Data Source
AI summary
In various implementations, provided are systems and methods for operating a neural network that includes conditional structures. In some implementations, an integrated circuit can compute a result using a set of intermediate results, where the intermediate results are computed from the outputs of a hidden layer of the neural network. The integrated circuit can further test the result against a condition. The outcome of the test can determine a next layer that the integrated circuit is to execute, or can be used to determine that further execution of the neural network can be terminated.